Chain-of-Thought Prompting One of the most powerful strategies in engineering prompts is [Chain-of-Thought] (CoT). By simply appending the phrase 'Let's think step by step' to your query, you force the model to document its internal logic before providing an final answer. This reduces errors in mathematical and logical reasoning. Few-Shot Prompting Instead of just asking for a result, provide examples of the desired input-output pairing. This is known as [Few-Shot Prompting]. By giving the model two or three examples of how you want data structured, you ground the output in a specific pattern, drastically improving consistency. Practical Implementation Consider this example: 'Input: Customer complained about shipping. Label: Negative. Input: Product is great. Label: Positive. Input: Shipping was okay. Label:' By providing these labels, the model follows the established pattern to complete the final line. Reference Anthropic's Prompting Guide to see how this translates to enterprise-grade applications. FAQs How many examples should I include in few-shot prompting? Two to five examples are usually sufficient to establish a pattern. Why does CoT work better for logic? It forces the model to use more computational 'space' to arrive at the solution. Can I use this for creative writing? Yes, providing writing samples helps the model mimic your specific tone.
Lesson 2: Advanced Prompting Techniques
“Moving beyond basic queries with Chain-of-Thought and Few-Shot prompting.”
